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Leveraging Data, Blockchain and AI to Help Agriculture Meet Growing Global Demand

Given the scale of the world’s food supply, there aren’t many industries that lend themselves to the power of data science and analytics than agriculture. This is the thinking behind a new research paper from a group of data scientists who make a case for finding new ways to use blockchain, AI and API management to enable “smart agriculture.” The paper, “Agricultural Digital Transformation,” has been published in the OR/MS Today journal from the Institute for Operations Research and the Management Sciences (INFORMS).

Best of arXiv.org for AI, Machine Learning, and Deep Learning – May 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

New Io-Tahoe White Paper Designed to Help Prepare for CCPA

There’s no denying it. Big data, and the resulting applications and technology have not only made consumers’ lives easier, but have also created new revenue streams for enterprises across all sectors. That said, the explosion of data has also created concerns around data privacy and cyber security, and has gotten the attention of regulators. Download the new report, “6 Steps: Getting Ready for CCPA,” courtesy of Io-Tahoe, to learn more about today’s new data privacy regulations to better protect your enterprise.

Survey Reveals Widespread Lack of IT Planning for AIOps

A recent survey of IT, business, security and operations executives shows that 76 percent of IT teams have not yet implemented artificial intelligence technologies to improve data center operations, despite the benefits of AIOps for business efficiency. In addition, just over half of survey respondents still have no budgets planned for AIOps projects in the next one to three years. Further, the survey reveals that most IT leaders are still struggling to implement effective strategies for AIOps due to a lack of clarity about their own technology expectations and business objectives.

AI-Driven Data Catalogs: How to Find the Right one for Your Business

The commoditization of data has opened a world of opportunities up for countless enterprises. But as big data explodes, metadata initiatives are failing, and data discovery and retrieval is getting more and more difficult. A new white paper from IO-Tahoe explores data catalogs as a potential answer to this challenge.

Machine Learning Based Traffic Sign Recognition ‘Most Influential’ Innovation of Past Decade

A research paper which revolutionized how cars read traffic signs has been recognized as the ‘most influential over the decade’ at a ceremony in Tokyo. The ideas the paper put forward have now found their way into everything from autonomous cars to controversial upcoming changes in EU law.

AI’s Role in Unleashing Intelligent Sensing

The rise of artificial intelligence (AI) is unlocking a wave of new sensor applications and driving market demand for intelligent sensing – the ability to extract insights from sensor data. To guide innovation and investment in this fast-evolving market, the team at Lux Research, a leading provider of tech-enabled research and advisory services for technology innovation, took a deep dive into how and where enhanced AI analytics are rapidly improving the capabilities of software-defined sensors.

SparkCognition’s Darwin Machine Learning Platform Designed to Accelerate Data Science at Scale

As machine learning technology becomes more widely available on an enterprise scale, differentiating and studying which platform can be best for your business can be difficult. A new white paper from SparkCognition explores one of the solutions on the market that works to accelerate data science at scale. Its Darwin machine learning platform is designed to automate the building and deployment of models.

Survey: 96% of Enterprises Encounter Training Data Quality and Labeling Challenges in Machine Learning Projects

IDC predicts worldwide spending on artificial intelligence (AI) systems will reach $35.8 billion in 2019, and 84% of enterprises believe investing in AI will lead to greater competitive advantages (Statista). However, nearly eight out of 10 enterprise organizations currently engaged in AI and machine learning (ML) report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence, according to information released from Alegion.

AI Skills — 93% of Organizations Committed to AI but Skills Shortage Poses Considerable Challenge

Most organizations are fully invested in AI but more than half don’t have the required in-house skilled talent to execute their strategy, according to new research from SnapLogic. The study found that 93% of US and UK organizations consider AI to be a business priority and have projects planned or already in production. However, more than half of them (51%) acknowledge that they don’t have the right mix of skilled AI talent in-house to bring their strategies to life.